This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

Epidermal growth factor (EGF) is overexpressed in many cancers and is associated with worse prognosis. EGF binds to its cell surface receptor (EGFR), which induces EGFR phosphorylation. Phosphorylated EGFR (p-EGFR) is translocated into the nucleus, which increases cancer cell activity. Nicotine, which is one of the main components of tobacco, is absorbed through pulmonary alveoli and mucosal epithelia in the head and neck region by smoking and moves into the blood. Nicotine in blood binds to nicotinic acetylcholine receptor (nAChR) in the central nervous system and serves a crucial role in tobacco addiction. Although nAChR localization is thought to be limited in the nervous system, nAChR is present in a wide variety of non-neuronal cells, including cancer cells. Recent studies suggest that nicotine contributes to the metastasis and resistance to anti-cancer drugs of various cancer cells. However, it remains unknown whether head and neck squamous cell carcinoma (HNSCC) cells can utilize nicotine-nAChR signaling to metastasize and acquire resistance to anti-cancer drugs, even though the mucosal epithelia of the head and neck region are the primary sites of exposure to tobacco smoke. To the best of our knowledge, the present study is the first to demonstrate the role of nicotine in metastasis and anti-EGFR-therapy resistance of HNSCC. The present findings demonstrated that nicotine increased proliferation, migration, invasion, p-EGFR nuclear translocation and protein kinase B (Akt) phosphorylation in HNSCC cells. It was also demonstrated that nicotine restored cetuximab-inhibited proliferation, migration and invasion of HNSCC cells. Finally, an in vivo experiment revealed that nicotine increased lymph node metastasis of xenografted tumors, whereas an nAChR inhibitor suppressed lymph node metastasis and p-EGFR nuclear localization of xenografted tumors. Taken together, these results demonstrated that nicotine induced nuclear accumulation of p-EGFR, and activation of Akt signaling. These signaling pathways elevated the activities of HNSCC cells, causing lymph node metastasis and serving a role in cetuximab resistance.

Some progress has been made in development of methods to regenerate bone from cultured cells, however no method is put to practical use. Here, we developed methods to isolate, purify, and expand mesenchymal stem cells (MSCs) from mouse compact bone that may be used to regenerate bone in vivo. These cells were maintained in long-term culture and were capable of differentiating along multiple lineages, including chondrocyte, osteocyte, and adipocyte trajectories. We used standard cell isolation and culture methods to establish cell cultures from mouse compact bone and bone marrow. Cultures were grown in four distinct media to determine the optimal composition of culture medium for bone-derived MSCs. Putative MSCs were subjected to flow cytometry, alkaline phosphatase assays, immunohistochemical staining, and several differentiation assays to assess cell identity, protein expression, and developmental potential. Finally, we used an in vivo bone formation assay to determine whether putative MSCs were capable of regenerating bone. We found that compact bone of mice was a better source of MCSs than the bone marrow, that growth in plastic flasks served to purify MSCs from hematopoietic cells, and that MSCs grown in basic fibroblast growth factor (bFGF)-conditioned medium were, based on multiple criteria, superior to those grown in leukemia inhibitory factor-conditioned medium. Moreover, we found that the MSCs isolated from compact bone and grown in bFGF-conditioned medium were capable of supporting bone formation in vivo. The methods and results described here have implications for understanding MSC biology and for clinical purpose.

Sonic hedgehog (SHH) and its signaling have been identified in several human cancers, and increased levels of SHH expression appear to correlate with cancer progression. However, the role of SHH in the tumor microenvironment (TME) of oral squamous cell carcinoma (OSCC) is still unclear. No studies have compared the expression of SHH in different subtypes of OSCC and focused on the relationship between the tumor parenchyma and stroma. In this study, we analyzed SHH and expression of its receptor, Patched-1 (PTCH), in the TME of different subtypes of OSCC. Fifteen endophytic-type cases (ED type) and 15 exophytic-type cases (EX type) of OSCC were used. H&E staining, immunohistochemistry (IHC), double IHC, and double-fluorescent IHC were performed on these samples. ED-type parenchyma more strongly expressed both SHH and PTCH than EX-type parenchyma. In OSCC stroma, CD31-positive cancer blood vessels, CD68- and CD11b-positive macrophages, and α-smooth muscle actin-positive cancer-associated fibroblasts partially expressed PTCH. On the other hand, in EX-type stroma, almost no double-positive cells were observed. These results suggest that autocrine effects of SHH induce cancer invasion, and paracrine effects of SHH govern parenchyma-stromal interactions of OSCC. The role of the SHH pathway is to promote growth and invasion.